Operations Manager
Tetra Maritime
Lagos
Nigeria
July 3, 2026
Full-timeJuly 3, 2026
Job Description
Job Overview:
We're looking for a hands-on AWS AI Engineer to design, build, and deploy AI solutions that solve real business problems. You'll work at the intersection of cloud infrastructure and modern AI, turning foundation models and AWS-native services into production-grade applications.
Duties and Responsibilities:
Design & Build AI Architectures: Develop end-to-end AI solutions selecting the right paradigm for the problem, spanning classical ML, NLP, and GenAI. Own Generative AI & Agentic Workflows: Design and deploy multi-agent systems and RAG architectures using Amazon Bedrock, AgentCore, and Strands Agents (or open-source equivalents like LangGraph/CrewAI), integrating foundation models like Claude and Nova. Manage Traditional ML Lifecycle: Build, tune, and deploy classical ML and computer vision models on AWS SageMaker (Pipelines, batch/real-time endpoints) alongside managed AWS AI services (Rekognition, Textract etc.). Champion Responsible AI & Observability: Implement end-to-end evaluation, guardrails, and bias detection using Bedrock Guardrails and AgentCore Policy. Monitor model drift and system traces using AgentCore Observability, CloudWatch, and OpenTelemetry. Ship Reliable Software & MLOps: Write clean, async Python and Infrastructure-as-Code (AWS CDK or Terraform) to automate CI/CD pipelines, collaborating closely with DevSecOps and the product team to deploy private, production-grade applications.
Required Qualifications:
3–4 years in software/data engineering with at least 2+ years specialized in AI/ML, including at least one GenAI or agentic system shipped to production. Strong proficiency in Python; highly comfortable with async programming, decorators, and API-driven patterns. Hands-on experience with the AWS AI ecosystem. Solid grasp of LLM fundamentals: context windows, tool use/function calling, RAG vs. fine-tuning, and evaluation frameworks. Experience building with at least one agentic framework (e.g., Strands Agents, LangGraph, CrewAI). Familiarity with vector search and embedding pipelines (OpenSearch, pgvector, etc.). Comfort with Infrastructure-as-Code (AWS CDK or Terraform). Valid AWS Associate Level certification (e.g., Solutions Architect Associate or Machine Learning Engineer Associate).
Experience:
2 years
We're looking for a hands-on AWS AI Engineer to design, build, and deploy AI solutions that solve real business problems. You'll work at the intersection of cloud infrastructure and modern AI, turning foundation models and AWS-native services into production-grade applications.
Duties and Responsibilities:
Design & Build AI Architectures: Develop end-to-end AI solutions selecting the right paradigm for the problem, spanning classical ML, NLP, and GenAI. Own Generative AI & Agentic Workflows: Design and deploy multi-agent systems and RAG architectures using Amazon Bedrock, AgentCore, and Strands Agents (or open-source equivalents like LangGraph/CrewAI), integrating foundation models like Claude and Nova. Manage Traditional ML Lifecycle: Build, tune, and deploy classical ML and computer vision models on AWS SageMaker (Pipelines, batch/real-time endpoints) alongside managed AWS AI services (Rekognition, Textract etc.). Champion Responsible AI & Observability: Implement end-to-end evaluation, guardrails, and bias detection using Bedrock Guardrails and AgentCore Policy. Monitor model drift and system traces using AgentCore Observability, CloudWatch, and OpenTelemetry. Ship Reliable Software & MLOps: Write clean, async Python and Infrastructure-as-Code (AWS CDK or Terraform) to automate CI/CD pipelines, collaborating closely with DevSecOps and the product team to deploy private, production-grade applications.
Required Qualifications:
3–4 years in software/data engineering with at least 2+ years specialized in AI/ML, including at least one GenAI or agentic system shipped to production. Strong proficiency in Python; highly comfortable with async programming, decorators, and API-driven patterns. Hands-on experience with the AWS AI ecosystem. Solid grasp of LLM fundamentals: context windows, tool use/function calling, RAG vs. fine-tuning, and evaluation frameworks. Experience building with at least one agentic framework (e.g., Strands Agents, LangGraph, CrewAI). Familiarity with vector search and embedding pipelines (OpenSearch, pgvector, etc.). Comfort with Infrastructure-as-Code (AWS CDK or Terraform). Valid AWS Associate Level certification (e.g., Solutions Architect Associate or Machine Learning Engineer Associate).
Experience:
2 years
Info
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